{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "y8jweT2RZDe5"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.datasets import fetch_openml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HvDacGLdZDe-"
   },
   "outputs": [],
   "source": [
    "openml_frame = fetch_openml(data_id=42477, as_frame=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 530
    },
    "colab_type": "code",
    "id": "LEcyr60LbbrR",
    "outputId": "5ecab4f5-0b1a-426c-d09b-b354f539cf64"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**Author**: Yeh, I. C., & Lien, C. H\n",
      "**Source**: [original](https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients#) - 2016-01-26  \n",
      "**Please cite**: Yeh, I. C., & Lien, C. H. (2009). The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients. Expert Systems with Applications, 36(2), 2473-2480.\n",
      "\n",
      "   \n",
      "\n",
      "Data Set Information:\n",
      "\n",
      "This research aimed at the case of customersâ€™ default payments in Taiwan and compares the predictive accuracy of probability of default among six data mining methods. From the perspective of risk management, the result of predictive accuracy of the estimated probability of default will be more valuable than the binary result of classification - credible or not credible clients. Because the real probability of default is unknown, this study presented the novel Sorting Smoothing Method to estimate the real probability of default. With the real probability of default as the response variable (Y), and the predictive probability of default as the independent variable (X), the simple linear regression result (Y = A + BX) shows that the forecasting model produced by artificial neural network has the highest coefficient of determination; its regression intercept (A) is close to zero, and regression coefficient (B) to one. Therefore, among the six data mining techniques, artificial neural network is the only one that can accurately estimate the real probability of default.\n",
      "\n",
      "\n",
      "## Attribute Information:\n",
      "\n",
      "This research employed a binary variable, default payment (Yes = 1, No = 0), as the response variable. This study reviewed the literature and used the following 23 variables as explanatory variables:\n",
      "X1: Amount of the given credit (NT dollar): it includes both the individual consumer credit and his/her family (supplementary) credit.\n",
      "X2: Gender (1 = male; 2 = female).\n",
      "X3: Education (1 = graduate school; 2 = university; 3 = high school; 4 = others).\n",
      "X4: Marital status (1 = married; 2 = single; 3 = others).\n",
      "X5: Age (year).\n",
      "X6 - X11: History of past payment. We tracked the past monthly payment records (from April to September, 2005) as follows: X6 = the repayment status in September, 2005; X7 = the repayment status in August, 2005; . . .;X11 = the repayment status in April, 2005. The measurement scale for the repayment status is: -1 = pay duly; 1 = payment delay for one month; 2 = payment delay for two months; . . .; 8 = payment delay for eight months; 9 = payment delay for nine months and above.\n",
      "X12-X17: Amount of bill statement (NT dollar). X12 = amount of bill statement in September, 2005; X13 = amount of bill statement in August, 2005; . . .; X17 = amount of bill statement in April, 2005.\n",
      "X18-X23: Amount of previous payment (NT dollar). X18 = amount paid in September, 2005; X19 = amount paid in August, 2005; . . .;X23 = amount paid in April, 2005.\n",
      "\n",
      "\n",
      "## Relevant Papers:\n",
      "\n",
      "Yeh, I. C., & Lien, C. H. (2009). The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients. Expert Systems with Applications, 36(2), 2473-2480.\n",
      "\n",
      "Downloaded from openml.org.\n"
     ]
    }
   ],
   "source": [
    "print(openml_frame['DESCR'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ZctVpc-mbhBk"
   },
   "outputs": [],
   "source": [
    "data = openml_frame['data']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 224
    },
    "colab_type": "code",
    "id": "nn6-AFgacC7e",
    "outputId": "8b9796d4-611d-4ce9-e467-4099d0a92a85"
   },
   "outputs": [
    {
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       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "colab_type": "code",
    "id": "tclDL5n_bMp3",
    "outputId": "bfd2cabc-ea52-4797-f0ae-d5a20d08d0c7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['x1', 'x2', 'x3', 'x4', 'x5', 'x6', 'x7', 'x8', 'x9', 'x10', 'x11',\n",
       "       'x12', 'x13', 'x14', 'x15', 'x16', 'x17', 'x18', 'x19', 'x20', 'x21',\n",
       "       'x22', 'x23'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 100,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "id": "4TsZitgObAZP",
    "outputId": "4a95092b-c355-44fc-dc0c-ae7520721afe"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'frame', 'feature_names', 'target_names', 'DESCR', 'details', 'categories', 'url'])"
      ]
     },
     "execution_count": 101,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "openml_frame.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 408
    },
    "colab_type": "code",
    "id": "lmU8LufVxMFR",
    "outputId": "a72294ac-521e-47be-e47f-47759f77f776"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['x1',\n",
       " 'x2',\n",
       " 'x3',\n",
       " 'x4',\n",
       " 'x5',\n",
       " 'x6',\n",
       " 'x7',\n",
       " 'x8',\n",
       " 'x9',\n",
       " 'x10',\n",
       " 'x11',\n",
       " 'x12',\n",
       " 'x13',\n",
       " 'x14',\n",
       " 'x15',\n",
       " 'x16',\n",
       " 'x17',\n",
       " 'x18',\n",
       " 'x19',\n",
       " 'x20',\n",
       " 'x21',\n",
       " 'x22',\n",
       " 'x23']"
      ]
     },
     "execution_count": 129,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "openml_frame['feature_names']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 425
    },
    "colab_type": "code",
    "id": "_rIBYmlQkKNS",
    "outputId": "26627fc6-bd88-4bc7-fb1e-ea3e82450dfa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "x1     float64\n",
       "x2     float64\n",
       "x3     float64\n",
       "x4     float64\n",
       "x5     float64\n",
       "x6     float64\n",
       "x7     float64\n",
       "x8     float64\n",
       "x9     float64\n",
       "x10    float64\n",
       "x11    float64\n",
       "x12    float64\n",
       "x13    float64\n",
       "x14    float64\n",
       "x15    float64\n",
       "x16    float64\n",
       "x17    float64\n",
       "x18    float64\n",
       "x19    float64\n",
       "x20    float64\n",
       "x21    float64\n",
       "x22    float64\n",
       "x23    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 102,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "74J0AtXTkcse"
   },
   "outputs": [],
   "source": [
    "cat_cols = list(data.select_dtypes(include='category').columns)\n",
    "num_cols = list(data.select_dtypes(exclude='category').columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "kVtkFZ_elT4W",
    "outputId": "b7fd4431-c27a-418c-f943-34d7c698459f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 104,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Se2tEqoNZDfo"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "cat_encoder = OneHotEncoder()\n",
    "X1 = cat_encoder.fit_transform(data[cat_cols]).todense()\n",
    "scaler = StandardScaler()\n",
    "X2 = scaler.fit_transform(\n",
    "    data[num_cols]\n",
    ")\n",
    "X = np.concatenate([X1, X2], axis=1)\n",
    "feature_names = list(cat_encoder.get_feature_names()) + num_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "kJqqE1adhZ6Q",
    "outputId": "dd248a2f-aa67-4d21-c06e-03e1da7af71e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30000, 23)"
      ]
     },
     "execution_count": 106,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QQV1jYNiZDfr"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "target_dict = {val: num for num, val in enumerate(list(openml_frame['target'].unique()))}\n",
    "y = openml_frame['target'].apply(lambda x: target_dict[x]).astype('float').values\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.33, random_state=42\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LZn1Sh6WZDfu"
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import tensorflow_probability as tfp\n",
    "import matplotlib.pyplot as plt\n",
    "#import tensorflow.compat.v2 as tf\n",
    "tfd = tfp.distributions\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "RH_pH02KZDfw"
   },
   "outputs": [],
   "source": [
    "negloglik = lambda y, rv_y: -rv_y.log_prob(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3LDk8ojlZDf0"
   },
   "outputs": [],
   "source": [
    "# Specify the prior over `keras.layers.Dense` `kernel` and `bias`.\n",
    "def prior_trainable(kernel_size, bias_size=0, dtype=None):\n",
    "    n = kernel_size + bias_size\n",
    "    return tf.keras.Sequential([\n",
    "    tfp.layers.VariableLayer(n, dtype=dtype),\n",
    "    tfp.layers.DistributionLambda(lambda t: tfd.Independent(\n",
    "        tfd.Normal(loc=t, scale=1),\n",
    "        reinterpreted_batch_ndims=1)),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zJ9uvwAHZDf5"
   },
   "outputs": [],
   "source": [
    "# Specify the surrogate posterior over `keras.layers.Dense` `kernel` and `bias`.\n",
    "def posterior_mean_field(kernel_size, bias_size=0, dtype=None):\n",
    "    n = kernel_size + bias_size\n",
    "    c = np.log(np.expm1(1.))\n",
    "    return tf.keras.Sequential([\n",
    "    tfp.layers.VariableLayer(2 * n, dtype=dtype),\n",
    "    tfp.layers.DistributionLambda(lambda t: tfd.Independent(\n",
    "        tfd.Normal(\n",
    "            loc=t[..., :n],\n",
    "            scale=1e-5 + tf.nn.softplus(c + t[..., n:])\n",
    "        ),\n",
    "    reinterpreted_batch_ndims=1)),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "s-t7UAJvZDf9"
   },
   "outputs": [],
   "source": [
    "model = tf.keras.Sequential([\n",
    "    tfp.layers.DenseVariational(2, posterior_mean_field, prior_trainable, kl_weight=1/X.shape[0]),\n",
    "    tfp.layers.DistributionLambda(\n",
    "        lambda t: tfd.Normal(\n",
    "            loc=t[..., :1],\n",
    "            scale=1e-3 + tf.math.softplus(0.01 * t[...,1:])\n",
    "        )\n",
    "    ),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "3_iF235jZDgA",
    "outputId": "a9c30f82-467b-4848-c173-d9ec914365be"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f4260d83b00>"
      ]
     },
     "execution_count": 169,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.compile(\n",
    "    optimizer=tf.optimizers.Adam(learning_rate=0.01),\n",
    "    loss=negloglik\n",
    ")\n",
    "callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=5)\n",
    "model.fit(\n",
    "    X_train,\n",
    "    y_train,\n",
    "    validation_data=(X_test, y_test),\n",
    "    epochs=1000,\n",
    "    verbose=False,\n",
    "    callbacks=[callback]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0zlsfXa6ZDgD"
   },
   "outputs": [],
   "source": [
    "preds = model(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "Jc28MtALZDgH",
    "outputId": "4532d9f8-b0fd-4da4-c79b-0f795bfa5e04"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7041085670903314"
      ]
     },
     "execution_count": 171,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "roc_auc_score(y_test, preds.mean().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "1j6PyUOiZDgN",
    "outputId": "a90d34a9-707a-4dd1-dc2e-e4f2996c7e3c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SpearmanrResult(correlation=0.6026589006825015, pvalue=0.0)"
      ]
     },
     "execution_count": 172,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy\n",
    "scipy.stats.spearmanr(np.abs(y_test - preds.mean().numpy().squeeze()), preds.variance().numpy().squeeze())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 374
    },
    "colab_type": "code",
    "id": "IY1DtB6eZDgr",
    "outputId": "8125321e-4b31-4e9c-8c10-4c529758ce3b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "size=25\n",
    "params = {\n",
    "    'legend.fontsize': 'large',\n",
    "    'figure.figsize': (20, 8),\n",
    "    'axes.labelsize': size,\n",
    "    'axes.titlesize': size,\n",
    "    'xtick.labelsize': size * 0.75,\n",
    "    'ytick.labelsize': size * 0.75,\n",
    "    'axes.titlepad': 25\n",
    "}\n",
    "plt.rcParams.update(params)\n",
    "\n",
    "error = y_test - preds.mean().numpy().squeeze()\n",
    "ax = sns.regplot(\n",
    "    x=preds.variance().numpy().squeeze(),\n",
    "    y=error*error,\n",
    "    color='c'\n",
    ")\n",
    "plt.ylabel('error')\n",
    "plt.xlabel('variance');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 422
    },
    "colab_type": "code",
    "id": "x8jj6gCn0Be7",
    "outputId": "981e62c6-efe6-4489-e3b3-82711432711a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f425a6bf080>"
      ]
     },
     "execution_count": 174,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import pandas as pd\n",
    "\n",
    "cm = confusion_matrix(y_test, preds.mean().numpy() >= 0.5)\n",
    "cm = pd.DataFrame(data=cm / cm.sum(axis=0), columns=['False', 'True'], index=['False', 'True'])\n",
    "sns.heatmap(\n",
    "    cm,\n",
    "    fmt='.2f',\n",
    "    cmap='Blues',\n",
    "    annot=True,\n",
    "    annot_kws={'fontsize': 18}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UZMsK1ww2vlV"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "predicting default on credit card applications",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
